Heavy metal pollution of river water and eco-friendly remediation using potent microalgal species

Pollution of rivers is mainly caused by anthropogenic activities such as discharge of effluent from industrial facilities,maintenance of sewage/effluent treatment plants,and dumping of solid waste on river banks.This study dealt with the pollution issues of the Cooum River in the well-known city of Chennai in South India.Water samples from 27 locations were collected and analyzed for 12 elements,including Ba,B,and Al,as well as heavy metals such as Pb,Cr,Mn,Fe,Co,Ni,Cu,Zn,and Cd.The samples showed levels of these elements that exceeded World Health Organization recommendations.Pearson correlation analysis revealed the inter-dependency among elements,and the contribution of each element based on factor loadings showed its percentage contribution compared to others.Water samples from six significant locations were chosen for remediation with three algae:Chlorella vulgaris,Scenedesmus dimorphus,and Phormedium sp.The uptake of pollutants led to the continuous growth of algae during the incubation period of 15 d,effectively removing heavy metals from the river water.The increasing levels of algal counts and the chlorophyll a content confirmed the algal growth during the incubation period,followed by a declining stage after the incubation period.The scanning electron microscopic images of algae before and after the remediation showed no remarkable modification of morphological patterns.This study showed that the uptake of heavy metals using algae is an effective water pollution remediation measure,making the process practicable in the field on a large scale in the near future.

Real-Time Data Transmission with Data Carrier Support Value in Neighbor Strategic Collection in WSN

An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The method first discovers the routes between the data sensors and the sink node.Several factors are considered for each sensor node along the route,including energy,number of neighbours,previous transmissions,and energy depletion ratio.Considering all these variables,the Sink Reachable Support Measure and the Secure Communication Support Measure,the method evaluates two distinct measures.The method calculates the data carrier support value using these two metrics.A single route is chosen to collect data based on the value of data carrier support.It has contributed to the design of Secure Communication Support(SCS)Estimation.This has been measured according to the strategy of each hop of the route.The suggested method improves the security and efficacy of data collection in wireless sensor networks.The second stage uses the two-fish approach to build a trust model for secure data transfer.A sim-ulation exercise was conducted to evaluate the effectiveness of the suggested framework.Metrics,including PDR,end-to-end latency,and average residual energy,were assessed for the proposed model.The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework.

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the grid.The classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the grids.The TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them.At the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)models.Though some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,etc.into account.In this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in TEM.The proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence rate.Moreover,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading systems.In order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome.The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues.Biomedical image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,etc.At the same time,cervical cancer becomes a major reason for increased women’s mortality rate.But cervical cancer is an identified at an earlier stage using regular pap smear images.In this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)environment.The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition.In addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)technique.Moreover,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear images.Furthermore,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images.In order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev database.The experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images

Super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose,such as in low-field magnetic resonance imaging(MRI).However,image super-resolution reconstruction remains a difficult task because of the complexity and high textual requirements for diagnosis purpose.In this paper,we offer a deep learning based strategy for reconstructing medical images from low resolutions utilizing Transformer and generative adversarial networks(T-GANs).The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction.Furthermore,we weighted the combination of content loss,adversarial loss,and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN.In comparison to established measures like peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM),our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.

Solvation number, thermochemical parameter, and viscosity study of sweeteners in aqueous and non-aqueous media through ultrasonic measurements

Ultrasonic velocity measurements are used to elucidate various aspects of solvation chemistry,including solute–solute and solute–solvent interactions.Herein,an attempt is made to study a behavior of two sweeteners,D-fructose and D-sorbitol,in aqueous and non-aqueous media was attempted.D-fructose is a simple sugar found in many foods and can be consumed by diabetics and people suffering from hypoglycemia.D-Sorbitol is a sugar substitute used in diet foods,sugar-free chewing gum,mints,cough syrups,mouthwash,toothpaste etc.,D-sorbitol is an excellent humectant and texturizing agent that is also used in other products such as pharmaceuticals and cosmetics.The interactions between the solute and solvent molecules are explained in terms of the solvation numbers of both aqueous and non-aqueous solutions of D-fructose and D-sorbitol.The viscosity study correlates the viscosity of the solution with solvation;here,density,ultrasonic velocity,and viscosity of aqueous and non-aqueous solutions at various concentrations are measured at different temperatures ranging from 35 to 55℃.These parameters provide sufficient information on the interaction between molecules that may aid chemists in analyzing the mechanisms of the behavior of D-fructose and D-sorbitol in water and the water–ethanol medium through which they are consumed.The Fourier transforms infrared spectra of pure solvent,salt,and their solutions were recorded and analyzed for confirmation.

Dielectric confinement on exciton binding energy and nonlinear optical properties in a strained Zn_(1-x_(in))Mg_(x(in))Se/Zn_(1-x(out))Mg_(x(out))Se quantum well

The band offsets for a Zn1-xin Mgxin Se/Zn1-xout Mgxout Se quantum well heterostructure are determined using the model solid theory. The heavy hole exciton binding energies are investigated with various Mg alloy contents. The effect of mismatch between the dielectric constants between the well and the barrier is taken into account. The dependence of the excitonic transition energies on the geometrical confinement and the Mg alloy is discussed. Non-linear optical properties are determined using the compact density matrix approach. The linear, third order non-linear optical absorption coefficient values and the refractive index changes of the exciton are calculated for different concentrations of magnesium. The results show that the occurred blue shifts of the resonant peak due to the Mg incorporation give the information about the variation of two energy levels in the quantum well width.